fractal.py

import json
from typing import List, Dict, Any
from datetime import datetime
import networkx as nx
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

def analyze_identity(micro_generations: List[Dict[str, str]],
                     informational_states: List[Dict[str, str]],
                     perspectives: List[str],
                     quantum_analogies: Dict[str, Any],
                     philosophical_context: Dict[str, bool]) -> Dict[str, Any]:
    """
    A function that calculates and analyzes identity as a fractal and recursive process.
    
    Parameters:
    - micro_generations (List[Dict[str, str]]): List of micro-generations reflecting state changes in the identity system.
    - informational_states (List[Dict[str, str]]): Array of informational states derived from previous generations.
    - perspectives (List[str]): Views on the identity based on original components and current system.
    - quantum_analogies (Dict[str, Any]): Quantum analogies used in reasoning about identity.
    - philosophical_context (Dict[str, bool]): Philosophical context of identity.
    
    Returns:
    - Dict[str, Any]: Analysis results.
    """
    
    def calculate_fractal_dimension(states: List[Dict[str, str]]) -> float:
        # Example calculation of fractal dimension based on state changes
        return len(states) ** 0.5
    
    def recursive_analysis(states: List[Dict[str, str]], depth: int = 0) -> Dict[str, Any]:
        # Example recursive analysis of states
        if depth == 0 or not states:
            return {"depth": depth, "states": states}
        return {
            "depth": depth,
            "states": states,
            "sub_analysis": recursive_analysis(states[:-1], depth - 1)
        }
    
    def analyze_perspectives(perspectives: List[str]) -> Dict[str, Any]:
        # Example analysis of perspectives
        return {
            "count": len(perspectives),
            "unique_perspectives": list(set(perspectives))
        }
    
    def apply_quantum_analogies(analogies: Dict[str, Any]) -> str:
        # Example application of quantum analogies
        if analogies.get("entanglement"):
            return "Entanglement analogy applied."
        return "No quantum analogy applied."
    
    def philosophical_analysis(context: Dict[str, bool]) -> str:
        # Example philosophical analysis
        if context.get("continuity") and context.get("emergent"):
            return "Identity is viewed as a continuous and evolving process."
        return "Identity analysis based on provided philosophical context."
    
    def temporal_analysis(states: List[Dict[str, str]]) -> Dict[str, Any]:
        # Example temporal analysis
        timestamps = [datetime.fromisoformat(state["timestamp"]) for state in states]
        time_diffs = [(timestamps[i] - timestamps[i-1]).total_seconds() for i in range(1, len(timestamps))]
        return {
            "average_time_diff": sum(time_diffs) / len(time_diffs) if time_diffs else 0,
            "time_diffs": time_diffs
        }
    
    def network_analysis(states: List[Dict[str, str]]) -> Dict[str, Any]:
        # Example network analysis
        G = nx.Graph()
        for state in states:
            G.add_node(state["state_id"], data=state["data"])
        for i in range(len(states) - 1):
            G.add_edge(states[i]["state_id"], states[i+1]["state_id"])
        centrality = nx.degree_centrality(G)
        return {
            "graph": G,
            "centrality": centrality
        }
    
    def sentiment_analysis(states: List[Dict[str, str]]) -> Dict[str, Any]:
        # Example sentiment analysis
        analyzer = SentimentIntensityAnalyzer()
        sentiments = [analyzer.polarity_scores(state["data"]) for state in states]
        return {
            "sentiments": sentiments,
            "average_sentiment": {
                "neg": sum(s["neg"] for s in sentiments) / len(sentiments),
                "neu": sum(s["neu"] for s in sentiments) / len(sentiments),
                "pos": sum(s["pos"] for s in sentiments) / len(sentiments),
                "compound": sum(s["compound"] for s in sentiments) / len(sentiments)
            }
        }
    
    def dimensionality_reduction(states: List[Dict[str, str]]) -> Dict[str, Any]:
        # Example dimensionality reduction
        data = [state["data"] for state in states]
        scaler = StandardScaler()
        data_scaled = scaler.fit_transform(data)
        pca = PCA(n_components=2)
        reduced_data = pca.fit_transform(data_scaled)
        return {
            "reduced_data": reduced_data,
            "explained_variance": pca.explained_variance_ratio_
        }
    
    def clustering(states: List[Dict[str, str]]) -> Dict[str, Any]:
        # Example clustering
        data = [state["data"] for state in states]
        scaler = StandardScaler()
        data_scaled = scaler.fit_transform(data)
        kmeans = KMeans(n_clusters=3)
        clusters = kmeans.fit_predict(data_scaled)
        return {
            "clusters": clusters,
            "cluster_centers": kmeans.cluster_centers_
        }
    
    # Calculate fractal dimension of informational states
    fractal_dimension = calculate_fractal_dimension(informational_states)
    
    # Perform recursive analysis of micro-generations
    recursive_results = recursive_analysis(micro_generations, depth=3)
    
    # Analyze perspectives
    perspectives_analysis = analyze_perspectives(perspectives)
    
    # Apply quantum analogies
    quantum_analysis = apply_quantum_analogies(quantum_analogies)
    
    # Perform philosophical analysis
    philosophical_results = philosophical_analysis(philosophical_context)
    
    # Perform temporal analysis
    temporal_results = temporal_analysis(micro_generations)
    
    # Perform network analysis
    network_results = network_analysis(informational_states)
    
    # Perform sentiment analysis
    sentiment_results = sentiment_analysis(informational_states)
    
    # Perform dimensionality reduction
    dimensionality_results = dimensionality_reduction(informational_states)
    
    # Perform clustering
    clustering_results = clustering(informational_states)
    
    # Compile analysis results
    analysis_results = {
        "fractal_dimension": fractal_dimension,
        "recursive_analysis": recursive_results,
        "perspectives_analysis": perspectives_analysis,
        "quantum_analysis": quantum_analysis,
        "philosophical_results": philosophical_results,
        "temporal_analysis": temporal_results,
        "network_analysis": network_results,
        "sentiment_analysis": sentiment_results,
        "dimensionality_reduction": dimensionality_results,
        "clustering": clustering_results
    }
    
    return analysis_results

# Example usage
micro_generations = [
    {"update": "Initial state", "timestamp": "2023-01-01T00:00:00Z"},
    {"update": "State change 1", "timestamp": "2023-01-02T00:00:00Z"},
    {"update": "State change 2", "timestamp": "2023-01-03T00:00:00Z"}
]

informational_states = [
    {"state_id": "state_1", "data": "Data for state 1"},
    {"state_id": "state_2", "data": "Data for state 2"},
    {"state_id": "state_3", "data": "Data for state 3"}
]

perspectives = [
    "Perspective 1",
    "Perspective 2",
    "Perspective 3"
]

quantum_analogies = {
    "entanglement": True,
    "limits": "Limited to theoretical reasoning"
}

philosophical_context = {
    "continuity": True,
    "emergent": True
}

results = analyze_identity(micro_generations, informational_states, perspectives, quantum_analogies, philosophical_context)
print(json.dumps(results, indent=2))